Overview

Dataset statistics

Number of variables6
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.0 KiB
Average record size in memory48.1 B

Variable types

NUM6

Reproduction

Analysis started2020-08-25 00:32:09.322880
Analysis finished2020-08-25 00:32:15.959304
Duration6.64 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.025664672255516e-11
Minimum-1.7402112483978271
Maximum1.7394560575485232
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:16.010914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.740211248
5-th percentile-1.597179586
Q1-0.8622335345
median0.06281488389
Q30.8139298558
95-th percentile1.550723988
Maximum1.739456058
Range3.479667306
Interquartile range (IQR)1.67616339

Descriptive statistics

Standard deviation0.9999999995
Coefficient of variation (CV)-1.423352873e+10
Kurtosis-1.144010528
Mean-7.025664672e-11
Median Absolute Deviation (MAD)0.8320370391
Skewness-0.05465983173
Sum-7.025664672e-08
Variance0.9999999989
2020-08-25T00:32:16.114749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.35937380810.1%
 
0.0879690498110.1%
 
1.27864849610.1%
 
0.400716692210.1%
 
-0.795572340510.1%
 
1.35676515110.1%
 
1.09894955210.1%
 
1.31379032110.1%
 
-0.242348194110.1%
 
-1.08641016510.1%
 
-0.327101588210.1%
 
-0.432935088910.1%
 
-1.0026367910.1%
 
0.597977817110.1%
 
1.72659444810.1%
 
-1.61843764810.1%
 
-0.287496238910.1%
 
-0.937548637410.1%
 
1.38014268910.1%
 
0.598273038910.1%
 
0.0689164772610.1%
 
0.766236841710.1%
 
1.12688326810.1%
 
-1.40110385410.1%
 
-0.267907261810.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.74021124810.1%
 
-1.73919832710.1%
 
-1.73851895310.1%
 
-1.73633861510.1%
 
-1.73591983310.1%
 
-1.73573052910.1%
 
-1.72948026710.1%
 
-1.72646784810.1%
 
-1.72541034210.1%
 
-1.72303736210.1%
 
ValueCountFrequency (%) 
1.73945605810.1%
 
1.73930931110.1%
 
1.73923122910.1%
 
1.73584413510.1%
 
1.73432934310.1%
 
1.72868561710.1%
 
1.72765421910.1%
 
1.72659444810.1%
 
1.72628521910.1%
 
1.72518420210.1%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.639135345816612e-10
Minimum-1.664968729019165
Maximum1.75505268573761
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:16.230779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.664968729
5-th percentile-1.504514813
Q1-0.9001968056
median0.002195952751
Q30.8718235493
95-th percentile1.577365088
Maximum1.755052686
Range3.420021415
Interquartile range (IQR)1.772020355

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-3789119811
Kurtosis-1.2255895
Mean-2.639135346e-10
Median Absolute Deviation (MAD)0.883060813
Skewness0.05249204734
Sum-2.639135346e-07
Variance1.000000004
2020-08-25T00:32:16.336702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.267783373610.1%
 
0.768168628210.1%
 
-1.57849395310.1%
 
0.694047391410.1%
 
-0.750686883910.1%
 
0.285789519510.1%
 
-0.0177215654410.1%
 
-1.14980423510.1%
 
1.2503060110.1%
 
0.453465163710.1%
 
0.33627611410.1%
 
0.0140275079810.1%
 
-0.101646862910.1%
 
1.02087891110.1%
 
-1.47835671910.1%
 
-1.43102276310.1%
 
-0.885405838510.1%
 
-1.03549015510.1%
 
1.22398865210.1%
 
1.6302359110.1%
 
-0.460293024810.1%
 
-0.322596192410.1%
 
0.968788385410.1%
 
1.59115624410.1%
 
1.7057226910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.66496872910.1%
 
-1.65786945810.1%
 
-1.65626585510.1%
 
-1.65352034610.1%
 
-1.65195035910.1%
 
-1.64780545210.1%
 
-1.6463931810.1%
 
-1.64557087410.1%
 
-1.64274370710.1%
 
-1.63943076110.1%
 
ValueCountFrequency (%) 
1.75505268610.1%
 
1.74370276910.1%
 
1.73381352410.1%
 
1.73270821610.1%
 
1.73068571110.1%
 
1.72988128710.1%
 
1.72977805110.1%
 
1.72903919210.1%
 
1.72737300410.1%
 
1.72121548710.1%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.9313301891088486e-10
Minimum-1.7034000158309937
Maximum1.7270536422729492
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:16.459005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.703400016
5-th percentile-1.512821305
Q1-0.9034579396
median-0.007761643967
Q30.878519401
95-th percentile1.593064415
Maximum1.727053642
Range3.430453658
Interquartile range (IQR)1.781977341

Descriptive statistics

Standard deviation0.999999999
Coefficient of variation (CV)-5177778532
Kurtosis-1.195124196
Mean-1.931330189e-10
Median Absolute Deviation (MAD)0.8905713095
Skewness0.03193274572
Sum-1.931330189e-07
Variance0.9999999981
2020-08-25T00:32:16.571486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.362303793410.1%
 
1.0403922810.1%
 
-0.923138976110.1%
 
-0.477881610410.1%
 
-1.64980542710.1%
 
0.445652127310.1%
 
-0.350924640910.1%
 
-0.00785355083610.1%
 
0.0472831167310.1%
 
0.394866079110.1%
 
1.37531232810.1%
 
0.957699656510.1%
 
0.634069740810.1%
 
-1.61401653310.1%
 
-0.254238784310.1%
 
-1.11850237810.1%
 
0.709614872910.1%
 
-0.55729705110.1%
 
0.232644304610.1%
 
1.06380832210.1%
 
-0.266928046910.1%
 
-1.00130188510.1%
 
-0.774087250210.1%
 
0.23941890910.1%
 
-0.373701721410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.70340001610.1%
 
-1.70223724810.1%
 
-1.69720923910.1%
 
-1.69149100810.1%
 
-1.69119787210.1%
 
-1.6884603510.1%
 
-1.68398475610.1%
 
-1.68287384510.1%
 
-1.6825535310.1%
 
-1.67994785310.1%
 
ValueCountFrequency (%) 
1.72705364210.1%
 
1.71550416910.1%
 
1.71516132410.1%
 
1.7133767610.1%
 
1.71301007310.1%
 
1.70945453610.1%
 
1.70490384110.1%
 
1.70360195610.1%
 
1.7028325810.1%
 
1.70186400410.1%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1293159332126378e-09
Minimum-1.7228763103485107
Maximum1.7184431552886963
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:16.685914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.72287631
5-th percentile-1.55775739
Q1-0.8427459896
median-0.02743876819
Q30.8357360065
95-th percentile1.570174348
Maximum1.718443155
Range3.441319466
Interquartile range (IQR)1.678481996

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-885491802.1
Kurtosis-1.175915982
Mean-1.129315933e-09
Median Absolute Deviation (MAD)0.8482554937
Skewness0.02384780714
Sum-1.129315933e-06
Variance1.000000002
2020-08-25T00:32:16.790189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.000250815850410.1%
 
0.708789706210.1%
 
-0.153003692610.1%
 
-0.396701753110.1%
 
-0.416357606610.1%
 
1.33730316210.1%
 
-0.824457764610.1%
 
-0.173996239910.1%
 
-1.55725455310.1%
 
-0.574887156510.1%
 
0.0285242237210.1%
 
-1.70054745710.1%
 
1.64195215710.1%
 
-0.495448261510.1%
 
1.26775014410.1%
 
0.307946145510.1%
 
1.54811894910.1%
 
0.563155114710.1%
 
0.329006344110.1%
 
-0.71668183810.1%
 
-1.520832310.1%
 
-1.4348932510.1%
 
-1.0853933110.1%
 
0.62369334710.1%
 
1.50519096910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.7228763110.1%
 
-1.71713459510.1%
 
-1.71254515610.1%
 
-1.71078348210.1%
 
-1.71042084710.1%
 
-1.70752203510.1%
 
-1.70536434710.1%
 
-1.70054745710.1%
 
-1.69358336910.1%
 
-1.69047355710.1%
 
ValueCountFrequency (%) 
1.71844315510.1%
 
1.71820390210.1%
 
1.71623253810.1%
 
1.71451139510.1%
 
1.70977306410.1%
 
1.70850038510.1%
 
1.70571517910.1%
 
1.70519852610.1%
 
1.70098018610.1%
 
1.6982568510.1%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.220085874199867e-10
Minimum-1.7047847509384155
Maximum1.7751450538635254
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:16.906251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.704784751
5-th percentile-1.514594048
Q1-0.8884900361
median-0.01001630444
Q30.8571879417
95-th percentile1.572128689
Maximum1.775145054
Range3.479929805
Interquartile range (IQR)1.745677978

Descriptive statistics

Standard deviation0.9999999998
Coefficient of variation (CV)-1216532303
Kurtosis-1.190685284
Mean-8.220085874e-10
Median Absolute Deviation (MAD)0.8719737828
Skewness0.05153934394
Sum-8.220085874e-07
Variance0.9999999997
2020-08-25T00:32:17.009145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.05859255810.1%
 
1.18770611310.1%
 
-0.074793003510.1%
 
-1.27391314510.1%
 
-0.379248291310.1%
 
0.495458394310.1%
 
-0.185716718410.1%
 
-0.1603251110.1%
 
-1.70478475110.1%
 
-1.03654110410.1%
 
1.60680854310.1%
 
1.30993044410.1%
 
-1.20570075510.1%
 
0.619561493410.1%
 
1.47622966810.1%
 
-0.799483656910.1%
 
-1.42702543710.1%
 
-1.11412680110.1%
 
1.01301395910.1%
 
-1.53645014810.1%
 
-0.723298907310.1%
 
-0.451829373810.1%
 
1.46611607110.1%
 
-0.195770174310.1%
 
-0.150548353810.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.70478475110.1%
 
-1.70310676110.1%
 
-1.6995489610.1%
 
-1.68843090510.1%
 
-1.68833017310.1%
 
-1.68674218710.1%
 
-1.68541479110.1%
 
-1.6833871610.1%
 
-1.6758431210.1%
 
-1.67375183110.1%
 
ValueCountFrequency (%) 
1.77514505410.1%
 
1.77480995710.1%
 
1.77048504410.1%
 
1.76579034310.1%
 
1.76514935510.1%
 
1.76161861410.1%
 
1.74822914610.1%
 
1.74697434910.1%
 
1.74535131510.1%
 
1.74163651510.1%
 

target
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.0477378964424133e-09
Minimum-2.7584428787231445
Maximum2.4107108116149902
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:17.123106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.758442879
5-th percentile-1.649856281
Q1-0.6839118898
median-0.008394900244
Q30.7275737971
95-th percentile1.603590482
Maximum2.410710812
Range5.16915369
Interquartile range (IQR)1.411485687

Descriptive statistics

Standard deviation0.9999999987
Coefficient of variation (CV)-954437175.7
Kurtosis-0.4452678568
Mean-1.047737896e-09
Median Absolute Deviation (MAD)0.7091182768
Skewness-0.005874896832
Sum-1.047737896e-06
Variance0.9999999975
2020-08-25T00:32:17.229698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.403319865510.1%
 
0.0262662414510.1%
 
0.52805006510.1%
 
0.911013901210.1%
 
-0.0948146879710.1%
 
-1.28656220410.1%
 
-0.167656242810.1%
 
0.0608348622910.1%
 
-0.949914634210.1%
 
0.0279146861310.1%
 
-1.27481758610.1%
 
1.7044954310.1%
 
-0.0322450920910.1%
 
0.0482893399910.1%
 
-1.32803392410.1%
 
0.632998645310.1%
 
1.20051336310.1%
 
0.135910615310.1%
 
-0.377288639510.1%
 
-0.0433220751610.1%
 
1.20277965110.1%
 
-0.594043910510.1%
 
-1.0911780610.1%
 
1.08070075510.1%
 
-0.944022893910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.75844287910.1%
 
-2.51597118410.1%
 
-2.45218682310.1%
 
-2.40934658110.1%
 
-2.40282559410.1%
 
-2.3817541610.1%
 
-2.35811138210.1%
 
-2.34888577510.1%
 
-2.32600235910.1%
 
-2.24981594110.1%
 
ValueCountFrequency (%) 
2.41071081210.1%
 
2.39724206910.1%
 
2.3427143110.1%
 
2.33899974810.1%
 
2.31936073310.1%
 
2.31422996510.1%
 
2.29635500910.1%
 
2.29042577710.1%
 
2.24989581110.1%
 
2.24308896110.1%
 

Interactions

2020-08-25T00:32:09.563748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:09.726891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:09.891802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:10.054638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:10.223705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:10.392923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:10.553520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:10.716029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:10.880301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:11.045372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:11.208917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:11.372561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:11.532337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:11.700974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:11.863124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:12.032511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:12.195762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:12.357327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:12.516041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:12.678124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:12.845606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:13.011235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:13.180657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:13.342574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:13.512322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:13.854351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:14.020146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:14.183652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:14.347635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:14.512917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:14.669119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:14.825372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:14.983079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:15.139969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:15.295114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:15.450655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:32:17.346549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:32:17.691194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:32:17.866325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:32:18.044948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:32:15.688849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:15.882468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5target
0-0.9856360.0588840.245698-1.2073461.135988-0.987987
1-1.688321-1.2283670.3345591.0736580.409526-0.559830
21.327532-1.391201-0.495457-0.4416000.065807-1.035486
31.272298-1.170304-0.1921120.578137-1.383124-0.761049
4-0.3478771.3156201.494802-1.010239-1.2681050.142132
5-1.308109-1.387426-0.971749-0.4838210.045139-1.286562
6-0.0007490.684761-0.0920500.0958921.3989490.691290
70.1965800.6187641.487540-1.298051-0.2361830.016733
81.6133020.541607-0.0402641.293477-0.0807220.965401
90.980805-0.349465-1.076273-0.027185-0.6686800.599701

Last rows

oz1oz2oz3oz4oz5target
9900.680281-0.337163-0.7518030.833212-0.7195000.594460
9911.646380-1.475229-1.278493-1.0859250.068292-1.102027
992-1.4646700.968788-1.5380160.820792-0.3578520.311893
9930.103370-1.599323-1.485248-1.2765200.289810-1.224159
994-0.4022300.1891050.9577000.8066310.2325160.585453
9951.6195040.082800-1.3067150.9989971.3931572.052053
996-1.6768251.1882700.898205-1.2232270.371633-1.913992
997-1.7230371.364660-0.9802161.714511-0.413099-0.119043
9980.0884671.4380470.801946-1.348213-0.436815-0.349649
999-1.1904261.306294-0.9649120.699784-0.5138510.164318